PSCI 2270 - Week 8
Department of Political Science, Vanderbilt University
October 17, 2024
3-Page Proposal and OSF
Data collection methods
Discussion of two papers
Due: Next Thursday (October 24) before class
Aim: Brief description of your project that follows final write-up structure
Submission: To OSF (add me as collaborator) and post link on Brightspace
Background and Literature Review: 1-2 paragraphs
Research Question and Theory: 1-2 paragraphs
Setting/Context: 1 paragraph
Independent Variables: 1 paragraph
Dependent Variables (Outcomes of Interest): 1 paragraph
Measurement: 1-2 paragraphs
Possible Issues: 1-3 paragraphs
Why is it important to study your dependent variable? E.g. what are its general effects? Or are there normative reasons?
What do we know about the causes of your dependent variable?
What methods and data scholars use to study your dependent variable?
Which particular cases (countries, regions, social media, etc.) do they study?
JSTOR: High-quality, peer-reviewed journals and books; strong in humanities and social sciences, but limited access to recent publications and requires subscription for full access.
ScienceDirect: Extensive collection of scientific and technical research articles, but primarily focused on science and technology and requires subscription for full access.
Web of Science: Comprehensive citation database; useful for citation analysis, but requires institutional access and has a complex interface.
Semantic Scholar: AI-powered search engine that provides relevant literature and citation analysis, but coverage may not be as extensive as other databases and is primarily focused on scientific literature.
Research Rabbit: Helps discover related papers and visualize connections between research topics, but is a newer tool with evolving features and may require learning curve.
Elicit: AI tool for literature review that helps in finding and summarizing research papers, but is limited to specific research questions and may not cover all disciplines.
| Section | Content |
|---|---|
| Abstract | Short summary-make sure you understand this! |
| Introduction | 1. The questions the paper will try to answer 2. Why it’s important to know those answers 3. A summary of what the answers are and how they were found |
| Theory | 1. The outcome variable (thing to be explained or measured) 2. The independent variables (things that explain outcome) 3. Hypotheses about measure of or effects on outcome |
| Data/Methods | 1. How and what data is collected 2. How variables are measured using this data 3. Technique(s)/Method(s) used to test the hypotheses |
| Results | 1. Do estimated relationships correspond with hypotheses? 2. Statistical and substantive significance of estimates 3. Checks of alternative explanations |
| Conclusion | Broader implications for the field of study |
| Appendix/Replication archive | Usually online: all details needed to verify the procedures and results and possibly to replicate |
Resource for storing all materials related to your study (except data/replication materials)
Each project is stored in a repository with version control (?)
One of the largest storages for pre-analysis plans for projects
Let’s go ahead and create a project: osf.io
Document analysis: Use of any audio, visual, or written materials as a source of data
Interview data: Data that are collected from responses to questions posed by the researcher to a respondent
Firsthand observation: Data that may be collected by making observations in a field study or in a laboratory setting
Question: How can we study factors that affect protest participation?
Geographic Information Systems (GIS) are being applied with increasing frequency, and with increasing sophistication, in international relations and in political science more generally. Their benefits have been impressive: analyses that simply would not have been possible without GIS are now being completed, and the spatial component of international politics—long considered central but rarely incorporated analytically—has been given new emphasis. However, new methods face new challenges, and to apply GIS successfully, two specific issues need to be addressed: measurement validity and selection bias. Both relate to the challenge of conceptualizing nonspatial phenomena with the spatial tools of GIS. Significant measurement error can occur when the concepts that are coded as spatial variables are not, in fact, validly measured by the default data structure of GIS, and selection bias can arise when GIS systematically excludes certain types of units. Because these potential problems are hidden by the technical details of the method, GIS data sets and analyses can sometimes appear to overcome these challenges when, in fact, they fail to do so. Once these issues come to light, however, potential solutions become apparent—including some in existing applications in international relations and in other fields.
Vector data: Points, lines, and polygons to describe spatial features: a point for a feature at a single location, a line for a linear feature such as a road, or a polygon for a feature that covers a definable spatial area.
Raster data: Pixels, predefined equivalent-sized units that are then assigned a value for a single variable across the entire area covered by the data.
Stasavage, David. 2011. States of Credit: Size, Power, and the Development of European Polities. Princeton, NJ: Princeton University Press.
Starr, Harvey. 2013. On Geopolitics: Space, Place, and International Relations. Boulder, CO: Paradigm.
Cederman, Lars-Erik, Kristian Skrede Gleditsch, and Halvard Buhaug. 2013. Inequality, Grievances, and Civil War. New York: Cambridge University Press.
Measurement validity:
Selection bias:
What kind of geolocation or spatial data can we use to study determinants of protest activity?
Text has always been an important data source in political science. What has changed in recent years is the feasibility of investigating large amounts of text quantitatively. The internet provides political scientists with more data than their mentors could have imagined, and the research community is providing accessible text analysis software packages, along with training and support. As a result, text-as-data research is becoming mainstream in political science. Scholars are tapping new data sources, they are employing more diverse methods, and they are becoming critical consumers of findings based on those methods. In this article, we first describe the four stages of a typical text-as-data project. We then review recent political science applications and explore one important methodological challenge—topic model instability—in greater detail.
Classification: Unsupervised machine learning methods compare the similarity of documents based on co-occurring features
Scaling: Use texts to locate political actors on ideological space
Text Reuse: Explicitly value word sequencing in judging document similarity
Natural Language Processing: Moving from “whom?” to “who did what to whom?”
Measurement reliability:
Measurement validity:
Selection bias:
What kind of text data can we use to study determinants of protest activity?
This article addresses “why leave the office” questions, primarily through a discussion of exemplary works that draw on field research. The first section focuses on James Scott’s Weapons of the Weak, which is a classic work of field research in comparative politics. It then turns to some recent works that explore several related topics, using a combination of surveys, participant observation, and interviews. Other field research methods, trends toward natural and field experiments, and combinations of field methods and non-field methods are also discussed. The last portion of the article concentrates on the challenges that field researcher’s encounter, irrespective of the particular method they use.
Participant observation
In-depth interviews
Survey
Experimentation
Scott, James. 1987. “Weapons of the weak: everyday forms of peasant resistance”
Posner, Daniel. 2004. “The political salience of cultural di erence: why Chewas and Tumbukas are allies in Zambia and adversaries in Malawi.”
Chattopadhyay, R., and Duflo, E. 2004. “Women as policy makers: evidence from a randomized policy experiment in India.”
Wantchekon, Leonard. 2003. “Clientelism and voting behavior: evidence from a Weld experiment in Benin.”
Weinstein, Jeremy. 2006. “Inside rebellion: the politics of insurgent violence.”
Personal biases:
Selection bias:
Ethics:
What kind of field research can we use to study determinants of protest activity?